CN115984280A - Surface trace analysis method, device, equipment and medium based on IGBT device - Google Patents
Surface trace analysis method, device, equipment and medium based on IGBT device Download PDFInfo
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Abstract
The invention relates to a scratch detection technology, and discloses a surface mark analysis method based on an IGBT device, which comprises the following steps: acquiring an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtered image; performing edge detection on the filtered image to obtain scratch pixel points; performing region scanning on the scratch pixel points to obtain pixel blocks; and traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested. The invention also provides a surface trace analysis device, equipment and medium based on the IGBT device. The invention can improve the efficiency and accuracy of the existing surface trace analysis method.
Description
Technical Field
The invention relates to the field of scratch detection, in particular to a surface mark analysis method, device, equipment and medium based on an IGBT device.
Background
With the development of information technology, electronic components and devices as basic bottom layer hardware for information transmission and processing occupy a significant position in the production and application of electronic products, wherein, insulated Gate Bipolar Transistors (IGBTs) are widely introduced in strategic emerging industries such as household appliances, digital products, rail transit, clean power generation, new energy vehicles, smart grids and the like by virtue of the advantages of small driving power and low saturation voltage. Due to the continuous evolution of the demand of electronic products, the IGBT device is continuously developed towards the directions of small volume, thinning, sheet type, miniaturization and modularization, and the characteristics improve the quality of the electronic products, but bring about great difficulty for detection mechanisms and detection personnel; in addition, in the production and processing process of the IGBT device, complex process treatment is required, and under multiple process treatment, the surface of the IGBT device is inevitably damaged, which causes surface defects of the IGBT device and directly affects whether the product is qualified, so that the surface defect detection of the electronic component is a necessary process in the production and processing and reliability analysis process of the electronic product. The traditional scratch detection means of the industrial assembly line is a manual visual detection mode, and the method has the defects of high labor intensity of detection personnel, easy subjective influence, low efficiency and frequent missed detection and false detection; although manual detection is replaced by adopting a deep learning algorithm, the detection and real-time identification of scratches on the surface of the background with the complex texture with uneven brightness are realized, the required data volume is large, and the image labeling task is heavy; therefore, the traditional machine vision detection algorithm or the deep learning method has many defects in the scratch analysis of the IGBT device, and needs to be improved in precision and speed for the small-target scratch detection.
In conclusion, the existing surface trace analysis method has the problems of low efficiency and accuracy.
Disclosure of Invention
The invention provides a surface trace analysis method, a surface trace analysis device, surface trace analysis equipment and a surface trace analysis medium based on an IGBT device, and mainly aims to solve the problems of low efficiency and accuracy of the existing surface trace analysis method.
In order to achieve the above object, the present invention provides a surface trace analysis method based on an IGBT device, including:
acquiring an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtered image;
performing edge detection on the filtered image to obtain scratch pixel points;
performing region scanning on the scratch pixel points to obtain pixel clusters;
and traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested.
Optionally, the filtering the original image of the electronic device to be tested to obtain a filtered image includes:
acquiring a color channel value of the original image of the electronic device to be detected, and performing gray level conversion on the original image of the electronic device to be detected according to the color channel value to obtain a gray level image;
generating a gray scale coordinate graph according to the gray scale graph, performing Gaussian calculation according to the gray scale coordinate graph, and generating a standard Gaussian filter template according to the result of the Gaussian calculation;
and performing convolution calculation on the gray level image by using the Gaussian filtering template to obtain a filtering image.
Optionally, the performing gaussian calculation according to the gray scale coordinate map includes:
performing Gaussian calculation on the gray scale coordinate graph by using the following formula:
wherein,
expressed as two-dimensional gaussian template parameters; />
Expressed as a preset smoothness parameter; />
Expressed as the abscissa of a pixel point in the gray scale coordinate graph; />
And expressing the coordinates as the vertical coordinates of the pixel points in the gray scale coordinate graph.
Optionally, the performing edge detection on the filtered image to obtain scratch pixel points includes:
carrying out pixel derivation on the filtering image to obtain a gradient value and a corresponding gradient direction of each pixel point in the filtering image;
and screening the pixel points of the filtering image according to the gradient values and the corresponding gradient directions to obtain scratch pixel points.
Optionally, the performing pixel derivation on the filtered image to obtain a gradient value and a corresponding gradient direction of each pixel point in the filtered image includes:
pixel derivation of the filtered image using:
wherein,
the gradient values of pixel points in the filtering image in the horizontal direction are expressed; />
Expressing the gradient value of the pixel points in the filtering image in the vertical direction; />
A value represented as each pixel point in the filtered image; />
Representing as gradient values of pixels in said filtered image; />
And expressing the gradient direction corresponding to the gradient value of the pixel point in the filtering image.
Optionally, the pair of scratch pixel points is subjected to region scanning to obtain a pixel blob, including:
randomly selecting scratch pixel points as seed points, and taking the positions of the seed points as scanning starting points;
performing neighborhood scanning in a neighborhood range of the scanning starting point according to a preset neighborhood radius;
marking unmarked scratch pixel points scanned in the neighborhood range to obtain identification pixel points, and generating pixel lumps according to the identification pixel points;
selecting scratch pixel points with the maximum distance from the seed points from the marked scratch pixel points according to the results of the neighborhood scanning;
and taking the scratch pixel point with the maximum distance from the seed point as a new seed point, and continuing scanning in the field range of the new seed point until traversing is finished to obtain a plurality of pixel blocks.
Optionally, the traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested includes:
storing the pixel blob in a preset image container;
carrying out direction division on the image container according to the direction of the circumscribed rectangle of the pixel block to obtain a transverse block information container and a longitudinal block information container;
and respectively merging the transverse lump information container and the longitudinal lump information container according to a preset merging rule to obtain a scratch image of the electronic device to be tested.
In order to solve the above problems, the present invention further provides a surface trace analyzing apparatus based on an IGBT device, the apparatus including:
the filtering processing module is used for acquiring the original image of the electronic device to be detected and carrying out filtering processing on the original image of the electronic device to be detected to obtain a filtering image;
the edge detection module is used for carrying out edge detection on the filtered image to obtain scratch pixel points;
the area scanning module is used for carrying out area scanning on the scratch pixel points to obtain pixel lumps;
and the pixel cluster merging module is used for traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the IGBT device-based footprint analysis method described above.
In order to solve the above problem, the present invention further provides a computer readable storage medium, wherein at least one computer program is stored in the computer readable storage medium, and the at least one computer program is executed by a processor in an electronic device to implement the IGBT device-based surface trace analysis method described above.
According to the embodiment of the invention, the original image of the electronic device to be detected is subjected to filtering processing to obtain the filtering image, so that the original image of the electronic device to be detected can be subjected to noise reduction, the background area is blurred to enhance the scratch area to be extracted, and the sharp edge of the scratch area can be protected, so that the edge trend of the original image cannot be changed; the scratch pixel points are obtained by performing edge detection on the filtered image, so that smooth scratch edge pixel points can be obtained, scratches can be accurately positioned according to the edge pixel points, and the accuracy of the surface mark analysis method is improved; by carrying out region scanning on the scratch pixel points, the pixel block is obtained, the real-time requirement of edge detection can be met, scratch information can be completely extracted, and the accuracy of the intelligent surface trace analysis method is enhanced. Therefore, the surface trace analysis method, device, equipment and medium based on the IGBT device, provided by the invention, can solve the problems of low efficiency and accuracy of the existing surface trace analysis method.
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Fig. 1 is a schematic flow chart of a surface trace analysis method based on an IGBT device according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating filtering processing performed on an original image of the electronic device to be tested to obtain a filtered image according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating a process of performing region scanning on the scratch pixel point to obtain a pixel blob according to an embodiment of the present invention;
fig. 4 is a functional block diagram of a surface trace analyzing apparatus based on an IGBT device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the surface trace analysis method based on the IGBT device according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a surface trace analysis method based on an IGBT device. The execution subject of the method for analyzing the surface trace based on the IGBT device includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server, a terminal, and the like. In other words, the IGBT device-based surface trace analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a surface trace analysis method based on an IGBT device according to an embodiment of the present invention is shown. In this embodiment, the method for analyzing surface traces based on the IGBT device includes:
s1, obtaining an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtering image.
In the embodiment of the invention, the original image of the electronic device to be detected can be acquired by using a machine vision product appearance detection platform, and the machine vision product appearance detection platform is suitable for a platform for element quality detection, such as IGBT device surface trace detection and the like; the present invention may employ a high resolution CCD camera (CCD image sensor) that can convert light into electric charge and store and transfer the electric charge, thereby facilitating the use of digital image processing algorithms.
Referring to fig. 2, in the embodiment of the present invention, the filtering the original image of the electronic device to be tested to obtain a filtered image includes:
s21, obtaining a color channel value of the original image of the electronic device to be tested, and performing gray level conversion on the original image of the electronic device to be tested according to the color channel value to obtain a gray level image;
s22, generating a gray scale coordinate graph according to the gray scale graph, performing Gaussian calculation according to the gray scale coordinate graph, and generating a standard Gaussian filter template according to the result of the Gaussian calculation;
and S23, carrying out convolution calculation on the gray level image by using the Gaussian filtering template to obtain a filtering image.
In the embodiment of the invention, as the original image of the electronic device to be tested is an RGB image (red, green and blue three-channel image), the original image needs to be subjected to gray level conversion; the gray level conversion is to perform gray level calculation on the original image of the electronic device to be detected, calculate color values of three color channels of RGB in the original image of the electronic device to be detected by using a gray level function, and use the calculated gray level value as a pixel value of the original image of the electronic device to be detected so as to obtain a gray level image; convolution calculation is a weighted average process, and can perform equalization processing on the values of the pixels of the gray level image according to the standard Gaussian filter template.
In the embodiment of the invention, the gray scale conversion is performed by using the following formula:
wherein,
a gray value represented as the gray map; />
A red channel pixel value represented as the color channel value; />
A green channel pixel value represented as the color channel value; />
A blue channel pixel value represented as the color channel value.
In the embodiment of the invention, the gray scale coordinate graph is subjected to Gaussian calculation by using the following formula:
wherein,
expressed as two-dimensional gaussian template parameters; />
Expressed as a preset smoothness parameter; />
Expressed as the abscissa of a pixel point in the gray scale coordinate graph; />
And expressing the coordinates as the vertical coordinates of the pixel points in the gray scale coordinate graph.
Further, the value of the smoothing degree parameter σ also determines the width of the standard gaussian filter template (determines the degree of smoothing), and when the value of the smoothing degree parameter σ is larger, it indicates that the frequency band of the standard gaussian filter template is wider, and the degree of smoothing is better.
In the embodiment of the invention, the standard Gaussian filter template is a template simulating a Gaussian function, has symmetry, and has a numerical value which is continuously reduced from the center to the periphery, and the size of the standard Gaussian filter template can be 3 x 3; after the two-dimensional Gaussian template parameters are obtained, normalization processing needs to be carried out on the two-dimensional Gaussian template parameters to obtain the standard Gaussian filtering template; after normalization, the central pixel of the standard Gaussian filter template can be limited in a gray scale interval of 0 to 255 during convolution calculation, so that pixel calculation errors can be reduced, and the accuracy of filter processing is improved.
In the embodiment of the invention, the purpose of filtering processing is to filter noise signals in the original image of the electronic device to be detected, because the standard Gaussian filter template is rotationally symmetric and has the same smoothness degree in all directions, the edge trend of the original image of the electronic device to be detected cannot be changed, the function for performing Gaussian calculation is a single-value function, the anchor point of the Gaussian convolution kernel is an extreme value and is monotonically decreased in all directions, the anchor point pixels cannot be influenced by pixels far away from the anchor point, and the accuracy of edge detection can be ensured.
And S2, carrying out edge detection on the filtered image to obtain scratch pixel points.
In the embodiment of the invention, the edge detection adopts the Sobel operator to obtain the gradient of the filtering image, because the pixel value near the scratch edge in the filtering image has obvious sudden change, namely the change is maximum, namely the first derivative is maximum, the point with the maximum pixel change, namely the edge point of the scratch pixel, can be determined by finding the maximum first derivative by using the Sobel operator, the Sobel operator can accurately position the scratch edge, the scratch edge extracted after the edge detection is continuous, the inhibition capacity on noise is strong, the influence of uneven illumination and the like is small, and the accuracy of scratch analysis is improved.
In the embodiment of the present invention, the performing edge detection on the filtered image to obtain a scratch pixel point includes:
carrying out pixel derivation on the filtering image to obtain a gradient value and a corresponding gradient direction of each pixel point in the filtering image;
and screening the pixel points of the filtering image according to the gradient values and the corresponding gradient directions to obtain scratch pixel points.
In the embodiment of the present invention, the pixel derivation is performed on the filtered image by using the following formula:
wherein,
the gradient values of pixel points in the filtering image in the horizontal direction are expressed; />
Expressing the gradient value of the pixel points in the filtering image in the vertical direction; />
Expressed as a value for each pixel in the filtered image; />
Representing as gradient values of pixel points in the filtered image; />
And expressing the gradient direction corresponding to the gradient value of the pixel point in the filtering image.
In the embodiment of the invention, the scratch pixel points are obtained by intercepting gradient values of the pixel points in the filtering image along respective gradient directions, the central point of the filtering image is taken as a starting point, and gradient value sets g (i), (i =1,2, \ 8230;, n) of a series of pixel points are intercepted in the filtering image along the starting point; selecting the position of the maximum value g (imax) in the gradient value set g (i) as a boundary point, screening in the front and back directions of the point, stopping the selection when g (i) -g (i-1) < threshold (i < imax), wherein the threshold is approximately 0, screening out the points meeting the requirements as g (j), (j =1,2, \ 8230;, n) according to the judgment rule, and obtaining the discrete points as scratch pixel points.
And S3, carrying out region scanning on the scratch pixel points to obtain pixel blocks.
Referring to fig. 3, in the embodiment of the present invention, the performing area scanning on the scratch pixel point to obtain a pixel blob includes:
s31, randomly selecting scratch pixel points as seed points, and taking the positions of the seed points as scanning starting points;
s32, performing neighborhood scanning in a neighborhood range of the scanning starting point according to a preset neighborhood radius;
s33, marking unmarked scratch pixel points scanned in the neighborhood range to obtain identification pixel points, and generating pixel blocks according to the identification pixel points;
s34, selecting scratch pixel points with the largest distance from the seed points from the marked scratch pixel points according to the results of neighborhood scanning;
s35, taking the scratch pixel point with the largest distance from the seed point as a new seed point, and continuing to scan in the field range of the new seed point until the scratch pixel point is traversed to obtain a plurality of pixel clusters.
In the embodiment of the invention, the region scanning can adopt a k-neighborhood searching method, and the classification accuracy is enhanced by calculating the Euclidean distance from the scratch pixel point to the seed point and then classifying the scratch pixel point according to the Euclidean distance, so that the tolerance on abnormal values and noise in the scratch pixel point is high.
In the embodiment of the invention, the neighborhood radius of the seed point can be 50 pixels, and the scratch pixel point is scanned in rows; recording the seed points as a type of label, and skipping the scratch pixel points when encountering a marked scratch pixel point; when an unmarked scratch pixel point is encountered, marking a label which is the same as the seed point, namely a type of label, on the unmarked scratch pixel point, and calculating the distance between the current point and the seed point, wherein the Euclidean distance is adopted as a distance by adopting a distance measurement method; scanning neighborhood regions of the seed points in sequence, arranging each distance calculation result in sequence according to the size sequence, taking scratch pixel points farthest from the seed points as new seed points, recording the new seed points as second-class labels, continuing scanning in the neighborhood range of the new seed points, repeating the steps of labeling until no scratch pixel points which are not labeled exist in the neighborhood range of the new seed points, continuing neighborhood scanning of the next seed point, and so on until all the scratch pixel points are scanned; and combining the obtained scratch pixel points with the same label into a block to obtain the pixel block.
And S4, traversing and merging the pixel blocks to obtain a scratch image of the electronic device to be tested.
In an embodiment of the present invention, the traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested includes:
storing the pixel blob in a preset image container;
dividing the image container into directions according to the direction of the circumscribed rectangle of the pixel blob to obtain a transverse blob information container and a longitudinal blob information container;
and respectively merging the transverse lump information container and the longitudinal lump information container according to a preset merging rule to obtain a scratch image of the electronic device to be tested.
In an embodiment of the present invention, the image container may be a basic image container built in an OpenCV (open source computer vision library), for example, a Mat container, and information of the pixel tile may be structured, so that the information of the pixel tile can be operated.
In the embodiment of the present invention, the circumscribed rectangle of the pixel blob may be generated by drawing a contour circumscribed rectangle by using OpenCV, and the direction of the circumscribed rectangle is represented as the direction of the pixel blob corresponding to the circumscribed rectangle; the image container comprises a transverse blob information container and a longitudinal blob information container, wherein the transverse blob information container is used for storing the external rectangle corresponding to the pixel blob in a transverse direction, and similarly, the longitudinal blob information container is used for storing the external rectangle corresponding to the pixel blob in a longitudinal direction; the merge rule includes: taking the transverse blob information container as an example, setting a blob pitch threshold in the transverse blob information container as 300 pixels, performing traversal scanning on pixel blobs in the transverse blob information container by taking the blob pitch threshold as a scanning radius until the blob information contains unmarked scratch pixel points, setting the pixel blobs containing the unmarked scratch pixel points as seed blobs, querying the unmarked pixel blobs with the distance to the seed blobs smaller than 300 pixels in the transverse blob information container, merging the unmarked pixel blobs with the seed blob pitch smaller than 300 pixels to obtain a local pixel blob, and similarly obtaining the local pixel blobs in the longitudinal blob information container; and finally, splicing the local pixel clusters of the transverse information container and the longitudinal information container to obtain a scratch image of the electronic device to be tested.
The invention provides an intelligent surface trace analysis method based on an IGBT device, which comprises the steps of carrying out filtering processing on an original image of an electronic device to be detected to obtain a filtering image, reducing noise of the original image of the electronic device to be detected, blurring a background area to enhance a scratch area to be extracted, and protecting sharp edges of the scratch area so as not to change the edge trend of the original image; the scratch pixel points are obtained by performing edge detection on the filtered image, so that smooth scratch edge pixel points can be obtained, scratches can be accurately positioned according to the edge pixel points, and the accuracy of the surface mark analysis method is improved; by scanning the scratch pixel points in the region, the pixel block is obtained, the real-time requirement of edge detection can be met, scratch information can be completely extracted, and the accuracy of the intelligent surface mark analysis method is enhanced. Therefore, the IGBT device-based surface trace analysis method provided by the invention can solve the problems of low efficiency and accuracy of the existing surface trace analysis method.
Fig. 4 is a functional block diagram of a surface trace analysis apparatus based on an IGBT device according to an embodiment of the present invention.
The surface trace analysis device 400 based on the IGBT device can be installed in electronic equipment. According to the realized functions, the IGBT device-based surface trace analysis apparatus 400 may include a filtering processing module 401, an edge detection module 402, a region scanning module 403, and a pixel blob merging module 404. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the filtering processing module 401 is configured to obtain an original image of an electronic device to be detected, and perform filtering processing on the original image of the electronic device to be detected to obtain a filtered image;
the edge detection module 402 is configured to perform edge detection on the filtered image to obtain scratch pixel points;
the area scanning module 403 is configured to perform area scanning on the scratch pixel point to obtain a pixel blob;
the pixel cluster merging module 404 is configured to perform traversal merging on the pixel clusters to obtain scratch images of the electronic device to be tested.
In detail, when used, each module in the IGBT device-based surface trace analysis apparatus 400 according to the embodiment of the present invention adopts the same technical means as the IGBT device-based surface trace analysis method described in the drawings, and can produce the same technical effect, and details are not repeated here.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for analyzing a surface trace based on an IGBT device according to an embodiment of the present invention.
The electronic device 500 may include a processor 501, a memory 502, a communication bus 503, and a communication interface 504, and may further include a computer program, such as a surface trace analysis program based on IGBT devices, stored in the memory 502 and executable on the processor 501.
In some embodiments, the processor 501 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a table trace analysis program based on an IGBT device, etc.) stored in the memory 502 and calling data stored in the memory 502.
The memory 502 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 502 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 502 may also be an external storage device of the electronic device in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the electronic device. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a surface trace analysis program based on the IGBT device, but also temporarily store data that has been output or will be output.
The communication bus 503 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 502 and at least one processor 501 or the like.
The communication interface 504 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 shows only an electronic device with components, and those skilled in the art will appreciate that the configuration shown in fig. 5 does not constitute a limitation of the electronic device 500, and may include fewer or more components than shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 501 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The IGBT device-based surface trace analysis program stored in the memory 502 of the electronic device 500 is a combination of a plurality of instructions, and when executed in the processor 501, can implement:
acquiring an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtering image;
performing edge detection on the filtered image to obtain scratch pixel points;
performing region scanning on the scratch pixel points to obtain pixel blocks;
and traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested.
Specifically, the processor 501 may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not described herein again.
Further, the modules/units integrated with the electronic device 500 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtered image;
performing edge detection on the filtered image to obtain scratch pixel points;
performing region scanning on the scratch pixel points to obtain pixel blocks;
and traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A surface trace analysis method based on an IGBT device is characterized by comprising the following steps:
acquiring an original image of an electronic device to be tested, and filtering the original image of the electronic device to be tested to obtain a filtered image;
performing edge detection on the filtered image to obtain scratch pixel points;
performing region scanning on the scratch pixel points to obtain pixel blocks;
and traversing and merging the pixel clusters to obtain a scratch image of the electronic device to be tested.
2. The IGBT device-based surface trace analysis method according to claim 1, wherein the filtering the original image of the electronic device to be tested to obtain a filtered image includes:
acquiring a color channel value of the original image of the electronic device to be detected, and performing gray level conversion on the original image of the electronic device to be detected according to the color channel value to obtain a gray level image;
generating a gray scale coordinate graph according to the gray scale graph, performing Gaussian calculation according to the gray scale coordinate graph, and generating a standard Gaussian filter template according to the result of the Gaussian calculation;
and performing convolution calculation on the gray level image by using the Gaussian filtering template to obtain a filtering image.
3. The IGBT device-based footprint analysis method of claim 2, wherein said performing a gaussian calculation from said grayscale coordinate map comprises:
performing a Gaussian calculation on the gray scale coordinate graph by using the following formula:
wherein,
expressed as two-dimensional gaussian template parameters; />
Expressed as a preset smoothness parameter; />
Expressed as the abscissa of a pixel point in the gray scale coordinate graph; />
And expressing the vertical coordinates of the pixel points in the gray scale coordinate graph.
4. The IGBT device-based surface trace analysis method according to claim 1, wherein the edge detection of the filtered image to obtain a scratch pixel point comprises:
carrying out pixel derivation on the filtering image to obtain a gradient value and a corresponding gradient direction of each pixel point in the filtering image;
and screening the pixel points of the filtering image according to the gradient values and the corresponding gradient directions to obtain scratch pixel points.
5. The IGBT device-based surface trace analysis method of claim 4, wherein the pixel derivation of the filtered image to obtain a gradient value and a corresponding gradient direction for each pixel in the filtered image comprises:
pixel derivation of the filtered image using:
wherein,
representing the gradient value of the pixel points in the filtering image in the horizontal direction; />
The gradient values of pixel points in the filtering image in the vertical direction are expressed; />
A value represented as each pixel point in the filtered image; />
Representing as gradient values of pixels in said filtered image; />
And expressing the gradient direction corresponding to the gradient value of the pixel point in the filtering image.
6. The method for analyzing the surface trace based on the IGBT device according to claim 1, wherein the step of scanning the scratch pixel point to obtain a pixel block comprises the following steps:
randomly selecting scratch pixel points as seed points, and taking the positions of the seed points as scanning starting points;
performing neighborhood scanning in a neighborhood range of the scanning starting point according to a preset neighborhood radius;
marking unmarked scratch pixel points scanned in the neighborhood range to obtain identification pixel points, and generating pixel lumps according to the identification pixel points;
selecting scratch pixel points with the maximum distance from the seed points from the marked scratch pixel points according to the results of the neighborhood scanning;
and taking the scratch pixel point with the maximum distance from the seed point as a new seed point, and continuing scanning in the field range of the new seed point until traversing is finished to obtain a plurality of pixel blocks.
7. The IGBT device-based surface trace analysis method according to any one of claims 1 to 6, wherein the traversing and merging of the pixel clusters to obtain the scratch image of the electronic device to be tested comprises:
storing the pixel blob in a preset image container;
carrying out direction division on the image container according to the direction of the circumscribed rectangle of the pixel block to obtain a transverse block information container and a longitudinal block information container;
and respectively merging the transverse lump information container and the longitudinal lump information container according to a preset merging rule to obtain a scratch image of the electronic device to be tested.
8. A surface trace analysis device based on an IGBT device is characterized by comprising:
the filtering processing module is used for acquiring the original image of the electronic device to be detected and carrying out filtering processing on the original image of the electronic device to be detected to obtain a filtering image;
the edge detection module is used for carrying out edge detection on the filtered image to obtain scratch pixel points;
the area scanning module is used for carrying out area scanning on the scratch pixel points to obtain pixel clusters;
and the pixel cluster merging module is used for traversing and merging the pixel clusters to obtain the scratch image of the electronic device to be tested.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the IGBT device-based surface trace analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the IGBT device-based surface trace analysis method according to any one of claims 1 to 7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113518220A (en) * | 2021-04-15 | 2021-10-19 | 中山大学 | Intra-frame division method, device and medium based on oriented filtering and edge detection |
| CN113781402A (en) * | 2021-08-19 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method and device for detecting chip surface scratch defects and computer equipment |
| CN113781406A (en) * | 2021-08-20 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Scratch detection method and device for electronic component and computer equipment |
| CN114529459A (en) * | 2022-04-25 | 2022-05-24 | 东莞市兆丰精密仪器有限公司 | Method, system and medium for enhancing image edge |
| CN115456956A (en) * | 2022-08-19 | 2022-12-09 | 浙江华周智能装备有限公司 | Method and device for detecting scratches of liquid crystal display and storage medium |
-
2023
- 2023-03-20 CN CN202310271812.9A patent/CN115984280A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113518220A (en) * | 2021-04-15 | 2021-10-19 | 中山大学 | Intra-frame division method, device and medium based on oriented filtering and edge detection |
| CN113781402A (en) * | 2021-08-19 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method and device for detecting chip surface scratch defects and computer equipment |
| CN113781406A (en) * | 2021-08-20 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Scratch detection method and device for electronic component and computer equipment |
| CN114529459A (en) * | 2022-04-25 | 2022-05-24 | 东莞市兆丰精密仪器有限公司 | Method, system and medium for enhancing image edge |
| CN115456956A (en) * | 2022-08-19 | 2022-12-09 | 浙江华周智能装备有限公司 | Method and device for detecting scratches of liquid crystal display and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| 李家宏 等: ""基于k-邻域前景像素分析的划痕提取方法"", 《计算机应用》, vol. 37, no. 2, pages 176 - 177 * |
| 李家宏 等: "基于k-邻域前景像素分析的划痕提取方法", 《计算机应用》, vol. 37, no. 2, pages 176 - 177 * |
| 陈少杰;李柏林;刘甲甲;冯河洋;: "快速鲁棒的光学零件表面划痕亚像素检测方法研究", 计算机应用与软件, no. 11, pages 157 - 161 * |
| 陈少杰;李柏林;赵文川;熊鹰;刘甲甲;: "一种快速鲁棒的光学表面划痕高精度检测方法", 激光技术, no. 03, pages 377 - 382 * |
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